Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1221542 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.
2024-06-23 | GSE263498 | GEO
Project description:Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.
2024-06-23 | GSE251926 | GEO
Project description:This project is based on the extension of the single cell transcriptomics atlas of mouse development towards E9.5. Inference of cell differentiation trajectories was carried out using Waddington-Optimal Transport, and the resulting computational reconstruction was contrasted with grafting experiments as well as complementary scRNA-seq experiments.
2023-01-01 | E-MTAB-11763 | biostudies-arrayexpress
Project description:This project is based on the extension of the single cell transcriptomics atlas of mouse development towards E9.5. Inference of cell differentiation trajectories was carried out using Waddington-Optimal Transport, and the resulting computational reconstruction was contrasted with grafting experiments as well as complementary scRNA-seq experiments. This data submission refers to the grafting experiments.
2024-01-31 | E-MTAB-13645 | biostudies-arrayexpress
Project description:Reconstruction of heterogeneity through single-cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we implement Spatial Transcriptomics to perform transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development and regeneration as well as its potential in pre-clinical and clinical pathology. Additional data to reproduce the data presented in this study with instrctions can be found at https://github.com/almaan/ST-mLiver and https://zenodo.org/record/4399655
2021-01-20 | GSE165141 | GEO
Project description:Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
| PRJNA1106439 | ENA
Project description:Here, we have developed a novel methodology called IRIS (Imaging Reconstruction using Indexed Sequencing) that enables cost-effective spatial transcriptomics profiling without relying on optical imaging. Through neighborhood interaction-based reconstruction, IRIS allows extensive analysis of large tissue sections and many replicates with adjustable mapping resolution at only a fraction of the cost of other commercial platforms. With the IRIS platform, we reconstructed a large area spatial area with two whole mouse brain coronal sections. Moreover, we also created a spatially resolved transcriptome atlas of the mouse brain and identified aging-associated changes in gene expression and spatial organization across various brain cell types. Further analysis of cell-cell interaction changes identified aging-associated foci in white matter regions enriched with inflammatory subtypes of microglia and oligodendrocytes. Overall, the IRIS methodology cost-effective and ease-of-use approach makes it broadly applicable to the studies of spatial gene expression changes in various systems.
2024-09-12 | GSE270383 | GEO
Project description:Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing based methods for cell lineage analysis depend on low resolution bulk analysis or rely on extensive single cell sequencing which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way towards large-scale human cell lineage discovery.
2016-04-18 | E-MTAB-4553 | biostudies-arrayexpress
Project description:Cellular barcoding using heritable synthetic barcodes coupled to high throughput sequencing is a powerful technique for the accurate tracing of clonal lineages in a wide variety of biological contexts. Recent studies have integrated cellular barcoding with a single-cell transcriptomics readout, extending the capabilities of these lineage tracing methods to the single-cell level. However there remains a lack of scalable and standardised open-source tools to pre-process and visualise both bulk and single-cell level cellular barcoding datasets. Here, we describe bartools, an open-source R-based toolkit that streamlines the pre-processing, analysis and visualisation of synthetic cellular barcoding datasets. In addition, we developed BARtab, a portable and scalable Nextflow pipeline that automates upstream barcode extraction, quality control, filtering and enumeration from high throughput sequencing data. In addition to population-level cellular barcoding datasets, BARtab and bartools contain methods for the extraction, annotation, and visualisation of transcribed barcodes from single-cell RNA-seq and spatial transcriptomics experiments, thus extending the analytical toolbox to also support novel expressed cellular barcoding methodologies. We showcase the integrated BARtab and bartools workflow through the analysis of bulk, single-cell, and spatial transcriptomics cellular barcoding datasets.
2023-11-05 | GSE246611 | GEO
Project description:The reconstruction of cell type- and patient-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important at the age of personalized medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied. Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays.. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks. To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism. By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic genes at pathway entry points such as transporters within the macrophage metabolic network. Analysis of these control points through further integration of metabolic and gene regulatory networks in various contexts could be beneficial in multiple fields from identification of disease intervention strategies to cellular reprogramming. ChIP-Seq was performed with chromatin from macrophages differentiated in vitro for 11 days from primary human CD14+ monocytes isolated from the blood of three different anonymous male donors. For each donor one ChIP sample using an antibody against H3K27ac and one input sample were sequenced. Please see the individual samples for further details.
2015-09-01 | E-GEOD-68798 | biostudies-arrayexpress